In [45]:
sns.boxplot(x=df['Total_price'])
plt.show()
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = pd.read_csv("D:\\IPSITA\\Chennai houseing sale.csv")
df=pd.DataFrame(data)
df.isnull().sum
<bound method NDFrame._add_numeric_operations.<locals>.sum of PRT_ID AREA INT_SQFT DATE_SALE DIST_MAINROAD N_BEDROOM \
0 False False False False False False
1 False False False False False False
2 False False False False False False
3 False False False False False False
4 False False False False False False
... ... ... ... ... ... ...
7104 False False False False False False
7105 False False False False False False
7106 False False False False False False
7107 False False False False False False
7108 False False False False False False
N_BATHROOM N_ROOM SALE_COND PARK_FACIL ... UTILITY_AVAIL STREET \
0 False False False False ... False False
1 False False False False ... False False
2 False False False False ... False False
3 False False False False ... False False
4 False False False False ... False False
... ... ... ... ... ... ... ...
7104 False False False False ... False False
7105 False False False False ... False False
7106 False False False False ... False False
7107 False False False False ... False False
7108 False False False False ... False False
MZZONE QS_ROOMS QS_BATHROOM QS_BEDROOM QS_OVERALL REG_FEE COMMIS \
0 False False False False False False False
1 False False False False False False False
2 False False False False False False False
3 False False False False False False False
4 False False False False False False False
... ... ... ... ... ... ... ...
7104 False False False False False False False
7105 False False False False False False False
7106 False False False False False False False
7107 False False False False False False False
7108 False False False False False False False
SALES_PRICE
0 False
1 False
2 False
3 False
4 False
... ...
7104 False
7105 False
7106 False
7107 False
7108 False
[7109 rows x 22 columns]>
df.fillna(-1, inplace=True)
df.head()
| PRT_ID | AREA | INT_SQFT | DATE_SALE | DIST_MAINROAD | N_BEDROOM | N_BATHROOM | N_ROOM | SALE_COND | PARK_FACIL | ... | UTILITY_AVAIL | STREET | MZZONE | QS_ROOMS | QS_BATHROOM | QS_BEDROOM | QS_OVERALL | REG_FEE | COMMIS | SALES_PRICE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | P03210 | Karapakkam | 1004 | 04-05-2011 | 131 | 1.0 | 1.0 | 3 | AbNormal | Yes | ... | AllPub | Paved | A | 4.0 | 3.9 | 4.9 | 4.330 | 380000 | 144400 | 7600000 |
| 1 | P09411 | Anna Nagar | 1986 | 19-12-2006 | 26 | 2.0 | 1.0 | 5 | AbNormal | No | ... | AllPub | Gravel | RH | 4.9 | 4.2 | 2.5 | 3.765 | 760122 | 304049 | 21717770 |
| 2 | P01812 | Adyar | 909 | 04-02-2012 | 70 | 1.0 | 1.0 | 3 | AbNormal | Yes | ... | ELO | Gravel | RL | 4.1 | 3.8 | 2.2 | 3.090 | 421094 | 92114 | 13159200 |
| 3 | P05346 | Velachery | 1855 | 13-03-2010 | 14 | 3.0 | 2.0 | 5 | Family | No | ... | NoSewr | Paved | I | 4.7 | 3.9 | 3.6 | 4.010 | 356321 | 77042 | 9630290 |
| 4 | P06210 | Karapakkam | 1226 | 05-10-2009 | 84 | 1.0 | 1.0 | 3 | AbNormal | Yes | ... | AllPub | Gravel | C | 3.0 | 2.5 | 4.1 | 3.290 | 237000 | 74063 | 7406250 |
5 rows × 22 columns
df['Total_price'] = df['REG_FEE'] + df['COMMIS'] + df['SALES_PRICE']
pd.to_datetime(df['DATE_BUILD'])
C:\Users\tuhin\AppData\Local\Temp\ipykernel_11436\2327747234.py:1: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing. pd.to_datetime(df['DATE_BUILD'])
0 1967-05-15
1 1995-12-22
2 1992-09-02
3 1988-03-18
4 1979-10-13
...
7104 1962-01-15
7105 1995-11-04
7106 1978-01-09
7107 1977-11-08
7108 1961-07-24
Name: DATE_BUILD, Length: 7109, dtype: datetime64[ns]
df['Year'] = df['DATE_BUILD'].apply(lambda x: x[6:10])
print(df)
PRT_ID AREA INT_SQFT DATE_SALE DIST_MAINROAD N_BEDROOM \
0 P03210 Karapakkam 1004 04-05-2011 131 1.0
1 P09411 Anna Nagar 1986 19-12-2006 26 2.0
2 P01812 Adyar 909 04-02-2012 70 1.0
3 P05346 Velachery 1855 13-03-2010 14 3.0
4 P06210 Karapakkam 1226 05-10-2009 84 1.0
... ... ... ... ... ... ...
7104 P03834 Karapakkam 598 03-01-2011 51 1.0
7105 P10000 Velachery 1897 08-04-2004 52 3.0
7106 P09594 Velachery 1614 25-08-2006 152 2.0
7107 P06508 Karapakkam 787 03-08-2009 40 1.0
7108 P09794 Velachery 1896 13-07-2005 156 3.0
N_BATHROOM N_ROOM SALE_COND PARK_FACIL ... MZZONE QS_ROOMS \
0 1.0 3 AbNormal Yes ... A 4.0
1 1.0 5 AbNormal No ... RH 4.9
2 1.0 3 AbNormal Yes ... RL 4.1
3 2.0 5 Family No ... I 4.7
4 1.0 3 AbNormal Yes ... C 3.0
... ... ... ... ... ... ... ...
7104 1.0 2 AdjLand No ... RM 3.0
7105 2.0 5 Family Yes ... RH 3.6
7106 1.0 4 Normal Sale No ... I 4.3
7107 1.0 2 Partial Yes ... RL 4.6
7108 2.0 5 Partial Yes ... I 3.1
QS_BATHROOM QS_BEDROOM QS_OVERALL REG_FEE COMMIS SALES_PRICE \
0 3.9 4.9 4.330 380000 144400 7600000
1 4.2 2.5 3.765 760122 304049 21717770
2 3.8 2.2 3.090 421094 92114 13159200
3 3.9 3.6 4.010 356321 77042 9630290
4 2.5 4.1 3.290 237000 74063 7406250
... ... ... ... ... ... ...
7104 2.2 2.4 2.520 208767 107060 5353000
7105 4.5 3.3 3.920 346191 205551 10818480
7106 4.2 2.9 3.840 317354 167028 8351410
7107 3.8 4.1 4.160 425350 119098 8507000
7108 3.5 4.3 3.640 349177 79812 9976480
Total_price Year
0 8124400 1967
1 22781941 1995
2 13672408 1992
3 10063653 1988
4 7717313 1979
... ... ...
7104 5668827 1962
7105 11370222 1995
7106 8835792 1978
7107 9051448 1977
7108 10405469 1961
[7109 rows x 24 columns]
df['Year'] = df['Year'].astype(int)
df['AgeOfProperty'] = 2023 - df['Year']
print(df)
PRT_ID AREA INT_SQFT DATE_SALE DIST_MAINROAD N_BEDROOM \
0 P03210 Karapakkam 1004 04-05-2011 131 1.0
1 P09411 Anna Nagar 1986 19-12-2006 26 2.0
2 P01812 Adyar 909 04-02-2012 70 1.0
3 P05346 Velachery 1855 13-03-2010 14 3.0
4 P06210 Karapakkam 1226 05-10-2009 84 1.0
... ... ... ... ... ... ...
7104 P03834 Karapakkam 598 03-01-2011 51 1.0
7105 P10000 Velachery 1897 08-04-2004 52 3.0
7106 P09594 Velachery 1614 25-08-2006 152 2.0
7107 P06508 Karapakkam 787 03-08-2009 40 1.0
7108 P09794 Velachery 1896 13-07-2005 156 3.0
N_BATHROOM N_ROOM SALE_COND PARK_FACIL ... QS_ROOMS QS_BATHROOM \
0 1.0 3 AbNormal Yes ... 4.0 3.9
1 1.0 5 AbNormal No ... 4.9 4.2
2 1.0 3 AbNormal Yes ... 4.1 3.8
3 2.0 5 Family No ... 4.7 3.9
4 1.0 3 AbNormal Yes ... 3.0 2.5
... ... ... ... ... ... ... ...
7104 1.0 2 AdjLand No ... 3.0 2.2
7105 2.0 5 Family Yes ... 3.6 4.5
7106 1.0 4 Normal Sale No ... 4.3 4.2
7107 1.0 2 Partial Yes ... 4.6 3.8
7108 2.0 5 Partial Yes ... 3.1 3.5
QS_BEDROOM QS_OVERALL REG_FEE COMMIS SALES_PRICE Total_price Year \
0 4.9 4.330 380000 144400 7600000 8124400 1967
1 2.5 3.765 760122 304049 21717770 22781941 1995
2 2.2 3.090 421094 92114 13159200 13672408 1992
3 3.6 4.010 356321 77042 9630290 10063653 1988
4 4.1 3.290 237000 74063 7406250 7717313 1979
... ... ... ... ... ... ... ...
7104 2.4 2.520 208767 107060 5353000 5668827 1962
7105 3.3 3.920 346191 205551 10818480 11370222 1995
7106 2.9 3.840 317354 167028 8351410 8835792 1978
7107 4.1 4.160 425350 119098 8507000 9051448 1977
7108 4.3 3.640 349177 79812 9976480 10405469 1961
AgeOfProperty
0 56
1 28
2 31
3 35
4 44
... ...
7104 61
7105 28
7106 45
7107 46
7108 62
[7109 rows x 25 columns]
sns.boxplot(x=df['Total_price'])
plt.show()
sns.scatterplot(x='N_BEDROOM', y='INT_SQFT', hue='Total_price', data=df)
plt.show()
correlation_matrix = df.corr()
print("Correlation Matrix:")
print(correlation_matrix)
Correlation Matrix:
INT_SQFT DIST_MAINROAD N_BEDROOM N_BATHROOM N_ROOM \
INT_SQFT 1.000000 0.002022 0.786340 0.515261 0.951279
DIST_MAINROAD 0.002022 1.000000 -0.002565 0.001969 0.002301
N_BEDROOM 0.786340 -0.002565 1.000000 0.755055 0.840280
N_BATHROOM 0.515261 0.001969 0.755055 1.000000 0.568564
N_ROOM 0.951279 0.002301 0.840280 0.568564 1.000000
QS_ROOMS 0.019850 0.002237 0.015109 0.013108 0.016524
QS_BATHROOM -0.008337 -0.029468 -0.007635 -0.012055 -0.007545
QS_BEDROOM 0.008865 0.001554 0.015566 0.013190 0.015072
QS_OVERALL 0.013989 -0.017255 0.014172 0.007877 0.015418
REG_FEE 0.657544 0.011600 0.455351 0.260249 0.630932
COMMIS 0.571076 0.010994 0.430418 0.256453 0.533343
SALES_PRICE 0.612125 0.018783 0.330999 0.108865 0.602760
Total_price 0.620010 0.018584 0.341310 0.118558 0.609345
Year -0.009301 0.005706 0.012426 0.001344 0.013409
AgeOfProperty 0.009301 -0.005706 -0.012426 -0.001344 -0.013409
QS_ROOMS QS_BATHROOM QS_BEDROOM QS_OVERALL REG_FEE \
INT_SQFT 0.019850 -0.008337 0.008865 0.013989 0.657544
DIST_MAINROAD 0.002237 -0.029468 0.001554 -0.017255 0.011600
N_BEDROOM 0.015109 -0.007635 0.015566 0.014172 0.455351
N_BATHROOM 0.013108 -0.012055 0.013190 0.007877 0.260249
N_ROOM 0.016524 -0.007545 0.015072 0.015418 0.630932
QS_ROOMS 1.000000 0.008828 0.007789 0.516147 0.019739
QS_BATHROOM 0.008828 1.000000 -0.011745 0.549056 -0.006719
QS_BEDROOM 0.007789 -0.011745 1.000000 0.628168 0.021526
QS_OVERALL 0.516147 0.549056 0.628168 1.000000 0.022410
REG_FEE 0.019739 -0.006719 0.021526 0.022410 1.000000
COMMIS 0.009377 -0.000471 0.020118 0.016946 0.659903
SALES_PRICE 0.021967 -0.011377 0.018804 0.020485 0.878148
Total_price 0.021888 -0.011122 0.019146 0.020720 0.888335
Year -0.018704 -0.021168 0.018011 -0.012200 0.108196
AgeOfProperty 0.018704 0.021168 -0.018011 0.012200 -0.108196
COMMIS SALES_PRICE Total_price Year AgeOfProperty
INT_SQFT 0.571076 0.612125 0.620010 -0.009301 0.009301
DIST_MAINROAD 0.010994 0.018783 0.018584 0.005706 -0.005706
N_BEDROOM 0.430418 0.330999 0.341310 0.012426 -0.012426
N_BATHROOM 0.256453 0.108865 0.118558 0.001344 -0.001344
N_ROOM 0.533343 0.602760 0.609345 0.013409 -0.013409
QS_ROOMS 0.009377 0.021967 0.021888 -0.018704 0.018704
QS_BATHROOM -0.000471 -0.011377 -0.011122 -0.021168 0.021168
QS_BEDROOM 0.020118 0.018804 0.019146 0.018011 -0.018011
QS_OVERALL 0.016946 0.020485 0.020720 -0.012200 0.012200
REG_FEE 0.659903 0.878148 0.888335 0.108196 -0.108196
COMMIS 1.000000 0.626275 0.642178 0.087359 -0.087359
SALES_PRICE 0.626275 1.000000 0.999649 0.116043 -0.116043
Total_price 0.642178 0.999649 1.000000 0.116524 -0.116524
Year 0.087359 0.116043 0.116524 1.000000 -1.000000
AgeOfProperty -0.087359 -0.116043 -0.116524 -1.000000 1.000000
C:\Users\tuhin\AppData\Local\Temp\ipykernel_11436\4063387690.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning. correlation_matrix = df.corr()
sns.pairplot(df)
plt.show()
